Testing places unique demands on AI. Errors carry real business risk, and fragile tests or slow updates can quickly erode trust in results. As a result, while momentum around AI in testing is strong, ...

Real-world deployments show 40% test cycle efficiency improvement, 50% faster regression testing, and 36% infrastructure cost savings.

AI is becoming a strategic differentiator in industrial automation; those who learn to apply it effectively will shape the next generation of industrial projects.

Quality engineering must evolve faster than code; otherwise, agentic AI will move quickly, learn rapidly and fail expensively.

The cost of not upping software quality assurance will be evident not only in the marketplace but on a company’s bottom line and in the lives of people.

As enterprises rethink their testing strategies, many teams are reviewing AI test automation tools that can help modernize QA workflows while keeping up with aggressive release schedules. These tools ...

BrowserStack today released its State of AI in Software Testing 2026 report, showing how AI has become central to modern testing, while highlighting the practical gaps that slow adoption.

Overview: AutoOps extends DevOps by embedding AI across coding, testing, deployment, monitoring, and optimization to create ...

New platform helps non-technical founders and teams implement AI inside real business workflows without hiring ...

From generating test cases and transforming test data to accelerating planning and improving developer communication, AI is having a profound impact on software testing. The integration of artificial ...